Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mixed-Integer Programming× | Multi-objective mixed-integer programming× | |
|---|---|---|
| Nyanja | Uigaji | Uigaji |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1958–1960 | 1980s–2000s |
| Mwanzilishi≠ | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) | Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization |
| Aina | Mathematical optimization | Mathematical optimization |
| Chanzo asilia≠ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 | Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin. ISBN: 9783540213987 |
| Majina mbadala | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming | MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally. | Multi-Objective Mixed-Integer Programming (MO-MIP) is an optimization framework that simultaneously optimizes two or more conflicting objective functions subject to linear or nonlinear constraints, where some decision variables are restricted to integer values and others are continuous. It is widely applied in engineering design, supply chain planning, resource allocation, and scheduling problems that require discrete choices alongside continuous quantities. |
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